Clinical and pathological correlations in male breast cancer (MBC): Intratumoral aromatase expression (ITA) via tissue microarray (TMA)
Bibliographic record
Abstract
10553 Background: Most cases of MBC are hormone-receptor (HR) positive and there is anecdotal evidence of disease response to aromatase inhibitors in the metastatic setting. Study objectives were to (i) assess clinical-pathologic characteristics in a cohort of MBC, (ii) evaluate intratumoral aromatase expression and (iii) assess its’ prognostic impact. Methods: A retrospective chart review was conducted for all cases of MBC seen at the Nova Scotia Cancer Centre between 1985–2005. Data abstracted included disease stage, tumor grade, HR status (ER/PR), HER2-neu and ITA expression as well as dates of disease recurrence and death. Tumor blocks were incorporated into 3 TMA’s with control specimens (gynecomastia, benign breast tissue, liver). Four 1.0 mm cores were taken of each tumor. Immunohistochemistry (IHC) was performed with the following antibodies; 6F11 ER primary Ab (Ventana, USA), 1A6 PR primary Ab (Ventana, USA), TAB250 monoclonal mouse anti-HER2 (Zymed, USA), DAKO 0185 rabbit anti-human c-erbB2 (DAKO, Canada), mouse anti-human p450 aromatase Ab (Cedarlane, Canada). ITA staining intensity and distribution was compared to benign hepatic tissue and if > or = to liver was called ’strong’ while < liver was called ’weak’. The log-rank test was used for survival comparisons. Kaplan-Meyer curves were used to estimate 5year disease-free (DFS) and overall (OS) survival probabilities. Results: 54 cases were identified with median age at diagnosis of 64 (31–85 yrs). Median tumor size was 2.6 cms (0.9–8cms) and 22 (41%) had nodal metastases. 45 cases had tissue available for IHC. Of these, 40 (89%) were ER and 33 (73%) were PR positive. HER2-neu was overexpressed in 4/42 (9.5%) and 12/45 (27%) had strong ITA expression. 5 yr DFS was 71% and OS was 64%. In univariate analysis strong (vs weak) ITA expression was associated with improved 5 yr OS (92% vs 49%, p = 0.038) but not DFS (82% vs 76%, p = 0.44) rates. Conclusion: The suggestion of improved OS but not DFS for those tumors with strong ITA expression may imply that these were more responsive to hormonal therapy in the metastatic setting or had a more indolent pace of progression. It is suggested that further investigation of ITA and response to hormonal therapy in MBC is warranted. No significant financial relationships to disclose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".